{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Batteries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jeroenderyck/anaconda/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import sklearn as sk\n",
    "import sklearn.linear_model as lm\n",
    "\n",
    "import sklearn.model_selection as split\n",
    "from sklearn.pipeline import Pipeline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Styling purpose\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set_context(\"talk\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Manual Reload Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import Credit_risk_classes as Classes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'Credit_risk_classes' from '/Users/jeroenderyck/Documents/Credit Models/Credit_risk_classes.py'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from importlib import reload\n",
    "reload(Classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load in Raw Datafile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jeroenderyck/anaconda/lib/python3.6/site-packages/numpy/lib/arraysetops.py:395: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  mask |= (ar1 == a)\n"
     ]
    }
   ],
   "source": [
    "# Processing will have to be in python\n",
    "data_raw=pd.read_csv(\"/Users/jeroenderyck/Documents/Data/CreditRiskModels/Credit_DATA.csv\",index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loans with only one observation are deleted from sample\n",
    "### DISCLAIMER : Optional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# --> clean loans with only one observation\n",
    "#DF is dataframe of raw dataset\n",
    "data_raw=data_raw[data_raw.groupby(\"masterloanidtrepp\")[\"observation_date\"].transform(\"count\")>1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RENAMING.\n",
    "### 1. Checked Data Dictionary for corresponding names. \n",
    "### 2. we only choose one appraisal value and look at percentage changes such that we dont need priors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Imputations\n",
    "data = (data_raw\n",
    "    .rename(columns={\n",
    "        \"bad_flag_final_v3\":\"Target_Response\",\n",
    "        'appvalue': 'pure_appraisal',\n",
    "        'fmrappvalue': 'total_value_property',\n",
    "        'gr_appvalue': 'pure_appraisal_Growth',\n",
    "        'obal': 'outstanding_scheduled_balance',\n",
    "        'origloanbal': 'original_loan_balance',\n",
    "        'mrfytdocc': 'percentage_occupied_rentspace',\n",
    "        'oltv': 'origination_loan_to_value',\n",
    "        'oterm': 'original_maturity',\n",
    "        'priorfyncf': 'most_recent_ncf',\n",
    "        'priorfydscrncf': 'recent_ncf_ratio_debtservice',\n",
    "        'priorfynoi': 'recent_noi',\n",
    "        'priorfyocc': 'recent_fiscal_occupied_rentspace',\n",
    "        'priorfydscr': 'most_recent_fiscal_debt_service',\n",
    "        'cltv_1': 'current_loan_to_value_indexed'\n",
    "    })\n",
    "    .drop([\"appvalue_prior\",\"mrappvalue\",\"gr_mrappvalue\",\"sample\",\"changeinvalue\",\"balact\",\"gr_balact\"\n",
    "        ,\"msa\"], axis=1)\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Missing Values Ranking \n",
    " -Rentarea and Units need to be imputed\n",
    " -if less than 1% missing --> can use mean and median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "units                               0.533306\n",
       "rentarea                            0.450721\n",
       "percentage_occupied_rentspace       0.450238\n",
       "recent_ncf_ratio_debtservice        0.299966\n",
       "most_recent_ncf                     0.298894\n",
       "recent_fiscal_occupied_rentspace    0.281603\n",
       "current_loan_to_value_indexed       0.262452\n",
       "most_recent_fiscal_debt_service     0.233555\n",
       "recent_noi                          0.226928\n",
       "debt_yield_p1                       0.186695\n",
       "pure_appraisal_Growth               0.039985\n",
       "original_loan_balance               0.036647\n",
       "balact_prior                        0.033695\n",
       "securappvalue                       0.012614\n",
       "securltv                            0.005727\n",
       "origination_loan_to_value           0.005727\n",
       "pure_appraisal                      0.005449\n",
       "total_value_property                0.005222\n",
       "corrected_ttm_months                0.000000\n",
       "Target_Response                     0.000000\n",
       "observation_date                    0.000000\n",
       "year                                0.000000\n",
       "outstanding_scheduled_balance       0.000000\n",
       "original_maturity                   0.000000\n",
       "division                            0.000000\n",
       "interestonly                        0.000000\n",
       "maturitytype                        0.000000\n",
       "rateindex                           0.000000\n",
       "segment_2                           0.000000\n",
       "segment_1_new                       0.000000\n",
       "masterloanidtrepp                   0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Classes.rank_nan(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the data.     \n",
    "\n",
    "1. __Training Versus Test set__  \n",
    "In order to test our models, we will need to divide the dataset in a training and test set.\n",
    "However we would like to keep loans in the same set, such that for example loan A belongs in the training set and loan B in the test set.        \n",
    "\n",
    "\n",
    "2. __Data splitter__\n",
    "The $DataSplitter$ class works like this. You give in the data and the target that needs to be classified predicted.  \n",
    "Addtionally you give in the group variable for Panel data\n",
    "It returns Datasplitter instance with attributes :\n",
    "  - Training_X\n",
    "  - Training_Y\n",
    "  - Testing_X\n",
    "  - Testing_Y\n",
    "  - Training_names = list with names of loans that belong in training set\n",
    "  - Testing names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Data_Splitter=Classes.Data_Splitter(data,Target=\"Target_Response\",\n",
    "                                    fraction_training=0.8,Group_variable=\"masterloanidtrepp\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Training_X,Training_Y= Data_Splitter.X_Training, Data_Splitter.Y_Training\n",
    "Testing_X, Testing_Y = Data_Splitter.X_Testing ,Data_Splitter.Y_Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Imputation\n",
    "We notice that $units$ and $rentarea$  must be closely related. We can impute them through a linear relationship\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17ee9d860>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dddVVsq/X7Xajry/0/aT097ujOgu/0TAePoyFD2MhxHj4xFMsttfUefvQnW20Y/v+upB9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\nERERUcj9f9q0ZNmH8um8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110d4c240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(data[\n",
    "    [\"units\",\"rentarea\"]]\n",
    "    .drop_duplicates().dropna()\n",
    "    .query(\" units <= 200\")\n",
    "    .query(\"1000 <= rentarea <= 200000\")\n",
    "    .plot(kind=\"scatter\",x=\"units\",y=\"rentarea\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "units    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[[\"units\"]].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-35-6ffe60eec0cb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m                                  \u001b[0mtraining_frame\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mData_Splitter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_Training\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                                  \u001b[0mindependent_variable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"rentarea\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m                                  \u001b[0mintercept\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m                                  )\n\u001b[1;32m      8\u001b[0m       )\n",
      "\u001b[0;32m~/Documents/Credit Models/Credit_risk_classes.py\u001b[0m in \u001b[0;36mimpute_linear_model\u001b[0;34m(self, target_column, training_frame, independent_variable, intercept, drop_outliers)\u001b[0m\n\u001b[1;32m    140\u001b[0m             \u001b[0msubset_Target_nan\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFrame\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFrame\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget_column\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m&\u001b[0m \u001b[0;34m~\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFrame\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindependent_variable\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0msubset_Target_nan\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtarget_column\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNotImplementedError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#Check results#Impute Units\n",
    "data =(Classes.Imputer(data)\n",
    "            .impute_linear_model(target_column=\"units\",\n",
    "                                 training_frame=Data_Splitter.X_Training,\n",
    "                                 independent_variable=\"rentarea\",\n",
    "                                 intercept=False,\n",
    "                                 )\n",
    "      )\n",
    "# Impute Rentarea\n",
    "data =(Classes.Imputer(data)\n",
    "            .impute_linear_model(target_column  = \"rentarea\",\n",
    "                                 training_frame = Data_Splitter.X_Training,\n",
    "                                 independent_variable = \"units\",\n",
    "                                 intercept=False\n",
    "                                 )\n",
    "      )\n",
    "\n",
    "#Check results\n",
    "Classes.rank_nan(data).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Impute $Original loan balance$ by median.       \n",
    "\n",
    "2. Impute $Pure apraisal growth$  by mean.  \n",
    "3. Create change in $Pure-$ and $Total appraisal$ as a new feauture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Classes.impute_med"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Fill original  loan balance with median of group loan\n",
    "#fill pure appraisal growth with mean of group loan\n",
    "data=Classes.impute_median(data,\"original_loan_balance\",Group_variable=\"masterloanidtrepp\")\n",
    "data=Classes.impute_mean(data,\"pure_appraisal_Growth\",Group_variable=\"masterloanidtrepp\")\n",
    "data= Classes.impute_mean(data,\"recent_ncf_ratio_debtservice\",Group_variable=\"masterloanidtrepp\")\n",
    "# Changes Pure and total Appraisal \n",
    "data=data.assign(Change__pure_appraisal=lambda x: x.groupby(\"masterloanidtrepp\")[\"pure_appraisal\"].pct_change())\n",
    "data=data.assign(Change_total_appraisal=lambda x: x.groupby(\"masterloanidtrepp\")[\"total_value_property\"].pct_change())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imputing original Loan Balance --> proxy original \n",
    "\n",
    "By the definition of the origination loan to value ratio we can extract the original loan balance:  \n",
    "\n",
    "$$OriginationLoanToValue=\\frac{OriginalLoanBalance}{Original AppraisalValue}$$\n",
    "\n",
    "\n",
    "$$OriginationLoanToValue \\times Original AppraisalValue=OriginalLoanBalance$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Check if origination loan to value makes sense with original appraisal value\n",
    "data_mortgage_values=data.groupby(\"masterloanidtrepp\")[\"pure_appraisal\"].first()*data.groupby(\"masterloanidtrepp\"\n",
    "                                                            )[\"origination_loan_to_value\"].first()/100\n",
    "\n",
    "data_mortgage = pd.DataFrame(data_mortgage_values,columns=[\"original_mortgage_value\"])\n",
    "\n",
    "# differences are not that big we add  a column to our dataframe because this one has no NAN\n",
    "if \"original_mortgage_value\" in data.columns:\n",
    "    pass\n",
    "else:\n",
    "    data= data.join(data_mortgage[[\"original_mortgage_value\"]],on=\"masterloanidtrepp\",how=\"left\")\n",
    "\n",
    "\n",
    "Median_original_mortgage =  data[\"original_mortgage_value\"].median()\n",
    "data=data.assign(original_mortgage_value=data[\"original_mortgage_value\"].fillna(Median_original_mortgage).values)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seaborn Pairplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "percentage_occupied_rentspace       0.450238\n",
       "most_recent_ncf                     0.298894\n",
       "recent_fiscal_occupied_rentspace    0.281603\n",
       "current_loan_to_value_indexed       0.262452\n",
       "most_recent_fiscal_debt_service     0.233555\n",
       "recent_noi                          0.226928\n",
       "debt_yield_p1                       0.186695\n",
       "Change__pure_appraisal              0.039363\n",
       "Change_total_appraisal              0.039138\n",
       "balact_prior                        0.033695\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Classes.rank_nan(data).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x113fa47f0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZZ4pjnimOiAiVlHqukpR9hAgRvTVvW0cXMNi8Po1+j83fJPFfPb8l/6qqKjZv3ozL5eLH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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11af16400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_relationship_plot=(data[[\"units\",\"rentarea\"]]\n",
    "                       .query(\"units <= 3000\"))\n",
    "                       \n",
    "\n",
    "\n",
    "sns.set()\n",
    "sns.pairplot(data_relationship_plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Fancy Impute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from fancyimpute import BiScaler, KNN, NuclearNormMinimization, SoftImpute\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols_to_transform = [ 'maturitytype', 'rateindex', 'segment_2', 'segment_1_new','division','interestonly']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_for_imputation= pd.get_dummies(data=data,columns=cols_to_transform).drop(\"observation_date\",axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[SoftImpute] Max Singular Value of X_init = 152776072824.215668\n",
      "[SoftImpute] Iter 1: observed MAE=129037.392223 rank=8\n",
      "[SoftImpute] Iter 2: observed MAE=128101.352269 rank=8\n",
      "[SoftImpute] Iter 3: observed MAE=128008.952880 rank=8\n",
      "[SoftImpute] Iter 4: observed MAE=127990.403668 rank=8\n",
      "[SoftImpute] Iter 5: observed MAE=127981.130654 rank=8\n",
      "[SoftImpute] Iter 6: observed MAE=127973.682516 rank=8\n",
      "[SoftImpute] Iter 7: observed MAE=127966.723564 rank=8\n",
      "[SoftImpute] Iter 8: observed MAE=127960.790980 rank=8\n",
      "[SoftImpute] Iter 9: observed MAE=127956.135212 rank=8\n",
      "[SoftImpute] Iter 10: observed MAE=127952.766274 rank=8\n",
      "[SoftImpute] Iter 11: observed MAE=127950.318954 rank=8\n",
      "[SoftImpute] Iter 12: observed MAE=127948.533024 rank=8\n",
      "[SoftImpute] Iter 13: observed MAE=127947.241886 rank=8\n",
      "[SoftImpute] Iter 14: observed MAE=127946.273896 rank=8\n",
      "[SoftImpute] Iter 15: observed MAE=127945.532359 rank=8\n",
      "[SoftImpute] Iter 16: observed MAE=127944.945077 rank=8\n",
      "[SoftImpute] Iter 17: observed MAE=127944.472031 rank=8\n",
      "[SoftImpute] Iter 18: observed MAE=127944.083522 rank=8\n",
      "[SoftImpute] Iter 19: observed MAE=127943.759609 rank=8\n",
      "[SoftImpute] Iter 20: observed MAE=127943.479266 rank=8\n",
      "[SoftImpute] Iter 21: observed MAE=127943.233029 rank=8\n",
      "[SoftImpute] Iter 22: observed MAE=127943.010048 rank=8\n",
      "[SoftImpute] Iter 23: observed MAE=127942.812753 rank=8\n",
      "[SoftImpute] Iter 24: observed MAE=127942.634856 rank=8\n",
      "[SoftImpute] Iter 25: observed MAE=127942.475738 rank=8\n",
      "[SoftImpute] Iter 26: observed MAE=127942.332797 rank=8\n",
      "[SoftImpute] Iter 27: observed MAE=127942.204030 rank=8\n",
      "[SoftImpute] Iter 28: observed MAE=127942.089772 rank=8\n",
      "[SoftImpute] Iter 29: observed MAE=127941.984513 rank=8\n",
      "[SoftImpute] Iter 30: observed MAE=127941.890250 rank=8\n",
      "[SoftImpute] Iter 31: observed MAE=127941.807021 rank=8\n",
      "[SoftImpute] Iter 32: observed MAE=127941.731467 rank=8\n",
      "[SoftImpute] Iter 33: observed MAE=127941.662128 rank=8\n",
      "[SoftImpute] Iter 34: observed MAE=127941.599065 rank=8\n",
      "[SoftImpute] Iter 35: observed MAE=127941.541457 rank=8\n",
      "[SoftImpute] Iter 36: observed MAE=127941.490221 rank=8\n",
      "[SoftImpute] Iter 37: observed MAE=127941.443597 rank=8\n",
      "[SoftImpute] Iter 38: observed MAE=127941.399647 rank=8\n",
      "[SoftImpute] Iter 39: observed MAE=127941.359260 rank=8\n",
      "[SoftImpute] Iter 40: observed MAE=127941.322528 rank=8\n",
      "[SoftImpute] Iter 41: observed MAE=127941.289060 rank=8\n",
      "[SoftImpute] Iter 42: observed MAE=127941.258057 rank=8\n",
      "[SoftImpute] Iter 43: observed MAE=127941.230556 rank=8\n",
      "[SoftImpute] Iter 44: observed MAE=127941.205920 rank=8\n",
      "[SoftImpute] Iter 45: observed MAE=127941.183213 rank=8\n",
      "[SoftImpute] Iter 46: observed MAE=127941.163092 rank=8\n",
      "[SoftImpute] Iter 47: observed MAE=127941.145354 rank=8\n",
      "[SoftImpute] Iter 48: observed MAE=127941.129414 rank=8\n",
      "[SoftImpute] Iter 49: observed MAE=127941.115156 rank=8\n",
      "[SoftImpute] Iter 50: observed MAE=127941.102803 rank=8\n",
      "[SoftImpute] Iter 51: observed MAE=127941.092259 rank=8\n",
      "[SoftImpute] Iter 52: observed MAE=127941.083212 rank=8\n",
      "[SoftImpute] Iter 53: observed MAE=127941.075386 rank=8\n",
      "[SoftImpute] Iter 54: observed MAE=127941.068665 rank=8\n",
      "[SoftImpute] Iter 55: observed MAE=127941.063208 rank=8\n",
      "[SoftImpute] Iter 56: observed MAE=127941.058373 rank=8\n",
      "[SoftImpute] Stopped after iteration 56 for lambda=3055521456.484313\n"
     ]
    }
   ],
   "source": [
    "data_filled_soft =SoftImpute().complete(data_for_imputation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_imputed = pd.DataFrame(data=data_filled_soft,columns=data_for_imputation.columns,index=data_for_imputation.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_imputed.to_csv(\"/Users/jeroenderyck/Desktop/Credit_Risk_Imputed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x116b76d30>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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85ZYpHjd+rbh1Ghu3Hfe4A3jjtuOsuLWnz6B3TQBJ/Nc+Sf5ChIgZGXHeb7ia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\nuoFwFyjCwpgzxj5XIS4nyX+Y9Ho1Wfp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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11af16208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_relationship_plot=(data[[\"units\",\"rentarea\",\"rateindex\"]]\n",
    "                       .query(\"units <= 3000\"))\n",
    "                       \n",
    "\n",
    "\n",
    "sns.set()\n",
    "sns.pairplot(data_relationship_plot,hue=\"rateindex\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "original_mortgage_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from fancyimpute import BiScaler, KNN, NuclearNormMinimization, SoftImpute\n",
    "\n",
    "# X is the complete data matrix\n",
    "# X_incomplete has the same values as X except a subset have been replace with NaN\n",
    "\n",
    "# Use 3 nearest rows which have a feature to fill in each row's missing features\n",
    "data_filled_knn = KNN(k=3).complete(data)\n",
    "\n",
    "# matrix completion using convex optimization to find low-rank solution\n",
    "# that still matches observed values. Slow!\n",
    "data_filled_nnm = NuclearNormMinimization().complete(data)\n",
    "\n",
    "# Instead of solving the nuclear norm objective directly, instead\n",
    "# induce sparsity using singular value thresholding\n",
    "data_filled_softimpute = SoftImpute().complete(data.normalize())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "splitter= Classes.Data_Splitter(Data=data_imputed,fraction_training=0.8,Group_variable=\"masterloanidtrepp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Training , Test= splitter.Training_frame, splitter.Validation_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>masterloanidtrepp</th>\n",
       "      <th>Target_Response</th>\n",
       "      <th>pure_appraisal</th>\n",
       "      <th>balact_prior</th>\n",
       "      <th>corrected_ttm_months</th>\n",
       "      <th>total_value_property</th>\n",
       "      <th>pure_appraisal_Growth</th>\n",
       "      <th>percentage_occupied_rentspace</th>\n",
       "      <th>outstanding_scheduled_balance</th>\n",
       "      <th>origination_loan_to_value</th>\n",
       "      <th>...</th>\n",
       "      <th>division_NorthEast-NewEngland</th>\n",
       "      <th>division_Other</th>\n",
       "      <th>division_South-Atlantic</th>\n",
       "      <th>division_South-EastSouthCentral</th>\n",
       "      <th>division_South-WestSouthCentral</th>\n",
       "      <th>division_West-Mountain</th>\n",
       "      <th>division_West-Pacific</th>\n",
       "      <th>interestonly_N</th>\n",
       "      <th>interestonly_P</th>\n",
       "      <th>interestonly_Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>122099.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5800000.0</td>\n",
       "      <td>2.239636e+06</td>\n",
       "      <td>285.0</td>\n",
       "      <td>3100000.0</td>\n",
       "      <td>0.071508</td>\n",
       "      <td>96.419000</td>\n",
       "      <td>3457959.12</td>\n",
       "      <td>60.14</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>122099.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5800000.0</td>\n",
       "      <td>2.234333e+06</td>\n",
       "      <td>282.0</td>\n",
       "      <td>3100000.0</td>\n",
       "      <td>0.071508</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>3448925.88</td>\n",
       "      <td>60.14</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>122099.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3100000.0</td>\n",
       "      <td>1.213714e+06</td>\n",
       "      <td>279.0</td>\n",
       "      <td>3100000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>1821866.24</td>\n",
       "      <td>60.14</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>122099.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3100000.0</td>\n",
       "      <td>1.213483e+06</td>\n",
       "      <td>275.0</td>\n",
       "      <td>3100000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.683325</td>\n",
       "      <td>1817318.72</td>\n",
       "      <td>60.14</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>122099.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5800000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>272.0</td>\n",
       "      <td>5800000.0</td>\n",
       "      <td>0.870968</td>\n",
       "      <td>1.683856</td>\n",
       "      <td>1812655.58</td>\n",
       "      <td>60.14</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   masterloanidtrepp  Target_Response  pure_appraisal  balact_prior  \\\n",
       "1           122099.0              0.0       5800000.0  2.239636e+06   \n",
       "2           122099.0              0.0       5800000.0  2.234333e+06   \n",
       "3           122099.0              0.0       3100000.0  1.213714e+06   \n",
       "4           122099.0              0.0       3100000.0  1.213483e+06   \n",
       "5           122099.0              0.0       5800000.0  0.000000e+00   \n",
       "\n",
       "   corrected_ttm_months  total_value_property  pure_appraisal_Growth  \\\n",
       "1                 285.0             3100000.0               0.071508   \n",
       "2                 282.0             3100000.0               0.071508   \n",
       "3                 279.0             3100000.0               0.000000   \n",
       "4                 275.0             3100000.0               0.000000   \n",
       "5                 272.0             5800000.0               0.870968   \n",
       "\n",
       "   percentage_occupied_rentspace  outstanding_scheduled_balance  \\\n",
       "1                      96.419000                     3457959.12   \n",
       "2                      92.000000                     3448925.88   \n",
       "3                      92.000000                     1821866.24   \n",
       "4                       1.683325                     1817318.72   \n",
       "5                       1.683856                     1812655.58   \n",
       "\n",
       "   origination_loan_to_value       ...        division_NorthEast-NewEngland  \\\n",
       "1                      60.14       ...                                  0.0   \n",
       "2                      60.14       ...                                  0.0   \n",
       "3                      60.14       ...                                  0.0   \n",
       "4                      60.14       ...                                  0.0   \n",
       "5                      60.14       ...                                  0.0   \n",
       "\n",
       "   division_Other  division_South-Atlantic  division_South-EastSouthCentral  \\\n",
       "1             0.0                      1.0                              0.0   \n",
       "2             0.0                      1.0                              0.0   \n",
       "3             0.0                      1.0                              0.0   \n",
       "4             0.0                      1.0                              0.0   \n",
       "5             0.0                      1.0                              0.0   \n",
       "\n",
       "   division_South-WestSouthCentral  division_West-Mountain  \\\n",
       "1                              0.0                     0.0   \n",
       "2                              0.0                     0.0   \n",
       "3                              0.0                     0.0   \n",
       "4                              0.0                     0.0   \n",
       "5                              0.0                     0.0   \n",
       "\n",
       "   division_West-Pacific  interestonly_N  interestonly_P  interestonly_Y  \n",
       "1                    0.0             1.0             0.0             0.0  \n",
       "2                    0.0             1.0             0.0             0.0  \n",
       "3                    0.0             1.0             0.0             0.0  \n",
       "4                    0.0             1.0             0.0             0.0  \n",
       "5                    0.0             1.0             0.0             0.0  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Training.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Logistic = lm.LogisticRegression().fit(X=Training.drop(\"Target_Response\",axis=1),y=Training[\"Target_Response\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Logistic = Classes.LogisticRegressionModel().fit(Training,Response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn.linear_model as lm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>masterloanidtrepp</th>\n",
       "      <th>Target_Response</th>\n",
       "      <th>pure_appraisal</th>\n",
       "      <th>balact_prior</th>\n",
       "      <th>corrected_ttm_months</th>\n",
       "      <th>total_value_property</th>\n",
       "      <th>pure_appraisal_Growth</th>\n",
       "      <th>percentage_occupied_rentspace</th>\n",
       "      <th>outstanding_scheduled_balance</th>\n",
       "      <th>origination_loan_to_value</th>\n",
       "      <th>...</th>\n",
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       "      <th>division_South-Atlantic</th>\n",
       "      <th>division_South-EastSouthCentral</th>\n",
       "      <th>division_South-WestSouthCentral</th>\n",
       "      <th>division_West-Mountain</th>\n",
       "      <th>division_West-Pacific</th>\n",
       "      <th>interestonly_N</th>\n",
       "      <th>interestonly_P</th>\n",
       "      <th>interestonly_Y</th>\n",
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       "      <td>0.000000e+00</td>\n",
       "      <td>274.0</td>\n",
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       "      <td>93.000000</td>\n",
       "      <td>16324415.51</td>\n",
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       "      <th>132</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>1400000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>271.0</td>\n",
       "      <td>1400000.0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>133</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>268.0</td>\n",
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       "      <td>93.000000</td>\n",
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       "      <td>24900000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>265.0</td>\n",
       "      <td>24900000.0</td>\n",
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       "      <td>93.000000</td>\n",
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       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>200012.0</td>\n",
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       "      <td>24900000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>262.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.109354</td>\n",
       "      <td>16140901.07</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>259.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.440000</td>\n",
       "      <td>16094275.51</td>\n",
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       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>256.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.071146</td>\n",
       "      <td>16046469.95</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>253.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.049621</td>\n",
       "      <td>15993268.83</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>250.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.025901</td>\n",
       "      <td>15934643.77</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>247.0</td>\n",
       "      <td>3480000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.546049</td>\n",
       "      <td>15882970.12</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>141</th>\n",
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       "      <td>24900000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>244.0</td>\n",
       "      <td>2880000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.707837</td>\n",
       "      <td>15829992.92</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>200012.0</td>\n",
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       "      <td>24900000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>241.0</td>\n",
       "      <td>199200000.0</td>\n",
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       "      <td>-42.266324</td>\n",
       "      <td>15771551.63</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>238.0</td>\n",
       "      <td>199200000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-42.292191</td>\n",
       "      <td>15707619.03</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>235.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.910890</td>\n",
       "      <td>15650389.00</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>232.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.887152</td>\n",
       "      <td>15591719.42</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>229.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.861173</td>\n",
       "      <td>15527510.34</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>226.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.832941</td>\n",
       "      <td>15457735.81</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>223.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.807311</td>\n",
       "      <td>15394389.96</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>220.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.781038</td>\n",
       "      <td>15329453.68</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>217.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.773902</td>\n",
       "      <td>15258897.29</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>214.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>15186708.04</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>1.539439e+07</td>\n",
       "      <td>210.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.595129</td>\n",
       "      <td>15116728.85</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>1.534992e+07</td>\n",
       "      <td>207.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.598557</td>\n",
       "      <td>15044998.08</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>1.525890e+07</td>\n",
       "      <td>204.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.585581</td>\n",
       "      <td>14967554.73</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>1.518671e+07</td>\n",
       "      <td>201.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.566489</td>\n",
       "      <td>14884375.81</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>1.511673e+07</td>\n",
       "      <td>198.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.544077</td>\n",
       "      <td>14806997.11</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>1.504500e+07</td>\n",
       "      <td>195.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.508074</td>\n",
       "      <td>14727685.73</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>200012.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>1.496755e+07</td>\n",
       "      <td>192.0</td>\n",
       "      <td>24900000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.507638</td>\n",
       "      <td>14642561.33</td>\n",
       "      <td>67.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389539</th>\n",
       "      <td>349200104.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21100000.0</td>\n",
       "      <td>6.554086e+06</td>\n",
       "      <td>118.0</td>\n",
       "      <td>21100000.0</td>\n",
       "      <td>0.032053</td>\n",
       "      <td>217.535924</td>\n",
       "      <td>1493445.23</td>\n",
       "      <td>7.10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389540</th>\n",
       "      <td>349200104.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21100000.0</td>\n",
       "      <td>1.486984e+06</td>\n",
       "      <td>115.0</td>\n",
       "      <td>21100000.0</td>\n",
       "      <td>0.032132</td>\n",
       "      <td>218.559427</td>\n",
       "      <td>1486984.25</td>\n",
       "      <td>7.10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389543</th>\n",
       "      <td>349200107.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4950000.0</td>\n",
       "      <td>5.657571e+06</td>\n",
       "      <td>117.0</td>\n",
       "      <td>4950000.0</td>\n",
       "      <td>0.031996</td>\n",
       "      <td>217.768138</td>\n",
       "      <td>1292505.92</td>\n",
       "      <td>26.20</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389544</th>\n",
       "      <td>349200107.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4950000.0</td>\n",
       "      <td>1.286996e+06</td>\n",
       "      <td>114.0</td>\n",
       "      <td>4950000.0</td>\n",
       "      <td>0.032064</td>\n",
       "      <td>218.650976</td>\n",
       "      <td>1286995.83</td>\n",
       "      <td>26.20</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389573</th>\n",
       "      <td>349300042.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7750000.0</td>\n",
       "      <td>8.397274e+06</td>\n",
       "      <td>117.0</td>\n",
       "      <td>7750000.0</td>\n",
       "      <td>0.031977</td>\n",
       "      <td>219.104490</td>\n",
       "      <td>5728000.00</td>\n",
       "      <td>73.91</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389574</th>\n",
       "      <td>349300042.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7750000.0</td>\n",
       "      <td>5.728000e+06</td>\n",
       "      <td>114.0</td>\n",
       "      <td>7750000.0</td>\n",
       "      <td>0.032019</td>\n",
       "      <td>219.645079</td>\n",
       "      <td>5728000.00</td>\n",
       "      <td>73.91</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389579</th>\n",
       "      <td>349300050.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6900000.0</td>\n",
       "      <td>7.866536e+06</td>\n",
       "      <td>118.0</td>\n",
       "      <td>6900000.0</td>\n",
       "      <td>0.031981</td>\n",
       "      <td>218.870667</td>\n",
       "      <td>4893486.48</td>\n",
       "      <td>71.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389580</th>\n",
       "      <td>349300050.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6900000.0</td>\n",
       "      <td>4.877454e+06</td>\n",
       "      <td>115.0</td>\n",
       "      <td>6900000.0</td>\n",
       "      <td>0.032028</td>\n",
       "      <td>219.469330</td>\n",
       "      <td>4877453.51</td>\n",
       "      <td>71.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389581</th>\n",
       "      <td>349300051.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7250000.0</td>\n",
       "      <td>7.388303e+06</td>\n",
       "      <td>118.0</td>\n",
       "      <td>7250000.0</td>\n",
       "      <td>0.031990</td>\n",
       "      <td>218.612382</td>\n",
       "      <td>4050000.00</td>\n",
       "      <td>55.86</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389582</th>\n",
       "      <td>349300051.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7250000.0</td>\n",
       "      <td>4.050000e+06</td>\n",
       "      <td>115.0</td>\n",
       "      <td>7250000.0</td>\n",
       "      <td>0.032039</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>4050000.00</td>\n",
       "      <td>55.86</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389589</th>\n",
       "      <td>350000008.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55600000.0</td>\n",
       "      <td>3.082914e+07</td>\n",
       "      <td>118.0</td>\n",
       "      <td>55600000.0</td>\n",
       "      <td>0.031918</td>\n",
       "      <td>228.910845</td>\n",
       "      <td>40000000.00</td>\n",
       "      <td>71.94</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389590</th>\n",
       "      <td>350000008.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55600000.0</td>\n",
       "      <td>4.000000e+07</td>\n",
       "      <td>115.0</td>\n",
       "      <td>55600000.0</td>\n",
       "      <td>0.031775</td>\n",
       "      <td>227.053552</td>\n",
       "      <td>40000000.00</td>\n",
       "      <td>71.94</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389593</th>\n",
       "      <td>350000017.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24500000.0</td>\n",
       "      <td>1.665235e+07</td>\n",
       "      <td>119.0</td>\n",
       "      <td>24500000.0</td>\n",
       "      <td>0.031992</td>\n",
       "      <td>223.022555</td>\n",
       "      <td>18400000.00</td>\n",
       "      <td>75.10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389594</th>\n",
       "      <td>350000017.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24500000.0</td>\n",
       "      <td>1.840000e+07</td>\n",
       "      <td>116.0</td>\n",
       "      <td>24500000.0</td>\n",
       "      <td>0.031965</td>\n",
       "      <td>222.668621</td>\n",
       "      <td>18400000.00</td>\n",
       "      <td>75.10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389599</th>\n",
       "      <td>350000021.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19900000.0</td>\n",
       "      <td>1.390639e+07</td>\n",
       "      <td>119.0</td>\n",
       "      <td>19900000.0</td>\n",
       "      <td>0.032013</td>\n",
       "      <td>221.821206</td>\n",
       "      <td>14100000.00</td>\n",
       "      <td>70.85</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389600</th>\n",
       "      <td>350000021.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19900000.0</td>\n",
       "      <td>1.410000e+07</td>\n",
       "      <td>116.0</td>\n",
       "      <td>19900000.0</td>\n",
       "      <td>0.032010</td>\n",
       "      <td>221.781998</td>\n",
       "      <td>14100000.00</td>\n",
       "      <td>70.85</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389603</th>\n",
       "      <td>350000027.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14400000.0</td>\n",
       "      <td>1.016070e+07</td>\n",
       "      <td>118.0</td>\n",
       "      <td>14400000.0</td>\n",
       "      <td>0.032044</td>\n",
       "      <td>220.149584</td>\n",
       "      <td>8170140.98</td>\n",
       "      <td>56.82</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389604</th>\n",
       "      <td>350000027.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14400000.0</td>\n",
       "      <td>8.140995e+06</td>\n",
       "      <td>115.0</td>\n",
       "      <td>14400000.0</td>\n",
       "      <td>0.032076</td>\n",
       "      <td>220.546452</td>\n",
       "      <td>8140994.87</td>\n",
       "      <td>56.82</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389607</th>\n",
       "      <td>350000034.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11350000.0</td>\n",
       "      <td>9.209471e+06</td>\n",
       "      <td>119.0</td>\n",
       "      <td>11350000.0</td>\n",
       "      <td>0.032045</td>\n",
       "      <td>219.795547</td>\n",
       "      <td>6800000.00</td>\n",
       "      <td>59.91</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389608</th>\n",
       "      <td>350000034.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11350000.0</td>\n",
       "      <td>6.800000e+06</td>\n",
       "      <td>116.0</td>\n",
       "      <td>11350000.0</td>\n",
       "      <td>0.032083</td>\n",
       "      <td>220.283520</td>\n",
       "      <td>6800000.00</td>\n",
       "      <td>59.91</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389615</th>\n",
       "      <td>350000040.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5900000.0</td>\n",
       "      <td>7.256266e+06</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5900000.0</td>\n",
       "      <td>0.032050</td>\n",
       "      <td>219.033920</td>\n",
       "      <td>3920000.00</td>\n",
       "      <td>66.44</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389616</th>\n",
       "      <td>350000040.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5900000.0</td>\n",
       "      <td>3.920000e+06</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5900000.0</td>\n",
       "      <td>0.032102</td>\n",
       "      <td>219.709590</td>\n",
       "      <td>3920000.00</td>\n",
       "      <td>66.44</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389617</th>\n",
       "      <td>350000042.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3100000.0</td>\n",
       "      <td>6.114630e+06</td>\n",
       "      <td>117.0</td>\n",
       "      <td>3100000.0</td>\n",
       "      <td>0.032055</td>\n",
       "      <td>218.572324</td>\n",
       "      <td>2204979.55</td>\n",
       "      <td>71.22</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389618</th>\n",
       "      <td>350000042.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3100000.0</td>\n",
       "      <td>2.197746e+06</td>\n",
       "      <td>114.0</td>\n",
       "      <td>3100000.0</td>\n",
       "      <td>0.032116</td>\n",
       "      <td>219.362561</td>\n",
       "      <td>2197745.64</td>\n",
       "      <td>71.22</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389619</th>\n",
       "      <td>350200005.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74500000.0</td>\n",
       "      <td>3.467797e+07</td>\n",
       "      <td>119.0</td>\n",
       "      <td>74500000.0</td>\n",
       "      <td>0.031962</td>\n",
       "      <td>230.187029</td>\n",
       "      <td>45000000.00</td>\n",
       "      <td>60.40</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389620</th>\n",
       "      <td>350200005.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74500000.0</td>\n",
       "      <td>4.500000e+07</td>\n",
       "      <td>116.0</td>\n",
       "      <td>74500000.0</td>\n",
       "      <td>0.031801</td>\n",
       "      <td>228.096599</td>\n",
       "      <td>45000000.00</td>\n",
       "      <td>60.40</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389633</th>\n",
       "      <td>350200020.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19975000.0</td>\n",
       "      <td>1.443034e+07</td>\n",
       "      <td>118.0</td>\n",
       "      <td>19975000.0</td>\n",
       "      <td>0.032024</td>\n",
       "      <td>222.207821</td>\n",
       "      <td>14982000.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389634</th>\n",
       "      <td>350200020.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19975000.0</td>\n",
       "      <td>1.498200e+07</td>\n",
       "      <td>115.0</td>\n",
       "      <td>19975000.0</td>\n",
       "      <td>0.032015</td>\n",
       "      <td>222.096102</td>\n",
       "      <td>14982000.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389637</th>\n",
       "      <td>350200022.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17300000.0</td>\n",
       "      <td>1.282776e+07</td>\n",
       "      <td>119.0</td>\n",
       "      <td>17300000.0</td>\n",
       "      <td>0.032036</td>\n",
       "      <td>221.506413</td>\n",
       "      <td>12470953.99</td>\n",
       "      <td>72.20</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389638</th>\n",
       "      <td>350200022.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17300000.0</td>\n",
       "      <td>1.243051e+07</td>\n",
       "      <td>116.0</td>\n",
       "      <td>17300000.0</td>\n",
       "      <td>0.032042</td>\n",
       "      <td>221.569980</td>\n",
       "      <td>12430505.73</td>\n",
       "      <td>72.20</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>279310 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         masterloanidtrepp  Target_Response  pure_appraisal  balact_prior  \\\n",
       "129               200012.0              0.0      24900000.0  1.081393e+07   \n",
       "130               200012.0              0.0      24900000.0  0.000000e+00   \n",
       "131               200012.0              0.0      24900000.0  0.000000e+00   \n",
       "132               200012.0              0.0       1400000.0  0.000000e+00   \n",
       "133               200012.0              0.0      24900000.0  0.000000e+00   \n",
       "134               200012.0              0.0      24900000.0  0.000000e+00   \n",
       "135               200012.0              0.0      24900000.0  0.000000e+00   \n",
       "136               200012.0              0.0      24900000.0  0.000000e+00   \n",
       "137               200012.0              0.0      24900000.0  0.000000e+00   \n",
       "138               200012.0              0.0      24900000.0  0.000000e+00   \n",
       "139               200012.0              0.0      24900000.0  0.000000e+00   \n",
       "140               200012.0              0.0      24900000.0  0.000000e+00   \n",
       "141               200012.0              0.0      24900000.0  0.000000e+00   \n",
       "142               200012.0              0.0      24900000.0  0.000000e+00   \n",
       "143               200012.0              0.0      24900000.0  0.000000e+00   \n",
       "144               200012.0              0.0      24900000.0  0.000000e+00   \n",
       "145               200012.0              0.0      24900000.0  0.000000e+00   \n",
       "146               200012.0              0.0      24900000.0  0.000000e+00   \n",
       "147               200012.0              0.0      24900000.0  0.000000e+00   \n",
       "148               200012.0              0.0      24900000.0  0.000000e+00   \n",
       "149               200012.0              0.0      24900000.0  0.000000e+00   \n",
       "150               200012.0              0.0      24900000.0  0.000000e+00   \n",
       "151               200012.0              0.0      24900000.0  0.000000e+00   \n",
       "152               200012.0              0.0      24900000.0  1.539439e+07   \n",
       "153               200012.0              0.0      24900000.0  1.534992e+07   \n",
       "154               200012.0              0.0      24900000.0  1.525890e+07   \n",
       "155               200012.0              0.0      24900000.0  1.518671e+07   \n",
       "156               200012.0              0.0      24900000.0  1.511673e+07   \n",
       "157               200012.0              0.0      24900000.0  1.504500e+07   \n",
       "158               200012.0              0.0      24900000.0  1.496755e+07   \n",
       "...                    ...              ...             ...           ...   \n",
       "1389539        349200104.0              0.0      21100000.0  6.554086e+06   \n",
       "1389540        349200104.0              0.0      21100000.0  1.486984e+06   \n",
       "1389543        349200107.0              0.0       4950000.0  5.657571e+06   \n",
       "1389544        349200107.0              0.0       4950000.0  1.286996e+06   \n",
       "1389573        349300042.0              0.0       7750000.0  8.397274e+06   \n",
       "1389574        349300042.0              0.0       7750000.0  5.728000e+06   \n",
       "1389579        349300050.0              0.0       6900000.0  7.866536e+06   \n",
       "1389580        349300050.0              0.0       6900000.0  4.877454e+06   \n",
       "1389581        349300051.0              0.0       7250000.0  7.388303e+06   \n",
       "1389582        349300051.0              0.0       7250000.0  4.050000e+06   \n",
       "1389589        350000008.0              0.0      55600000.0  3.082914e+07   \n",
       "1389590        350000008.0              0.0      55600000.0  4.000000e+07   \n",
       "1389593        350000017.0              0.0      24500000.0  1.665235e+07   \n",
       "1389594        350000017.0              0.0      24500000.0  1.840000e+07   \n",
       "1389599        350000021.0              0.0      19900000.0  1.390639e+07   \n",
       "1389600        350000021.0              0.0      19900000.0  1.410000e+07   \n",
       "1389603        350000027.0              0.0      14400000.0  1.016070e+07   \n",
       "1389604        350000027.0              0.0      14400000.0  8.140995e+06   \n",
       "1389607        350000034.0              0.0      11350000.0  9.209471e+06   \n",
       "1389608        350000034.0              0.0      11350000.0  6.800000e+06   \n",
       "1389615        350000040.0              0.0       5900000.0  7.256266e+06   \n",
       "1389616        350000040.0              0.0       5900000.0  3.920000e+06   \n",
       "1389617        350000042.0              0.0       3100000.0  6.114630e+06   \n",
       "1389618        350000042.0              0.0       3100000.0  2.197746e+06   \n",
       "1389619        350200005.0              0.0      74500000.0  3.467797e+07   \n",
       "1389620        350200005.0              0.0      74500000.0  4.500000e+07   \n",
       "1389633        350200020.0              0.0      19975000.0  1.443034e+07   \n",
       "1389634        350200020.0              0.0      19975000.0  1.498200e+07   \n",
       "1389637        350200022.0              0.0      17300000.0  1.282776e+07   \n",
       "1389638        350200022.0              0.0      17300000.0  1.243051e+07   \n",
       "\n",
       "         corrected_ttm_months  total_value_property  pure_appraisal_Growth  \\\n",
       "129                     280.0            24900000.0               0.000000   \n",
       "130                     277.0            24900000.0              -0.943775   \n",
       "131                     274.0            24900000.0               0.000000   \n",
       "132                     271.0             1400000.0               0.000000   \n",
       "133                     268.0             1400000.0               0.000000   \n",
       "134                     265.0            24900000.0              16.785714   \n",
       "135                     262.0            24900000.0               0.000000   \n",
       "136                     259.0            24900000.0               0.000000   \n",
       "137                     256.0            24900000.0               0.000000   \n",
       "138                     253.0            24900000.0               0.000000   \n",
       "139                     250.0            24900000.0               0.000000   \n",
       "140                     247.0             3480000.0               0.000000   \n",
       "141                     244.0             2880000.0               0.000000   \n",
       "142                     241.0           199200000.0               0.000000   \n",
       "143                     238.0           199200000.0               0.000000   \n",
       "144                     235.0            24900000.0               0.000000   \n",
       "145                     232.0            24900000.0               0.000000   \n",
       "146                     229.0            24900000.0               0.000000   \n",
       "147                     226.0            24900000.0               0.000000   \n",
       "148                     223.0            24900000.0               0.000000   \n",
       "149                     220.0            24900000.0               0.000000   \n",
       "150                     217.0            24900000.0               0.000000   \n",
       "151                     214.0            24900000.0               0.000000   \n",
       "152                     210.0            24900000.0               0.000000   \n",
       "153                     207.0            24900000.0               0.000000   \n",
       "154                     204.0            24900000.0               0.000000   \n",
       "155                     201.0            24900000.0               0.000000   \n",
       "156                     198.0            24900000.0               0.000000   \n",
       "157                     195.0            24900000.0               0.000000   \n",
       "158                     192.0            24900000.0               0.000000   \n",
       "...                       ...                   ...                    ...   \n",
       "1389539                 118.0            21100000.0               0.032053   \n",
       "1389540                 115.0            21100000.0               0.032132   \n",
       "1389543                 117.0             4950000.0               0.031996   \n",
       "1389544                 114.0             4950000.0               0.032064   \n",
       "1389573                 117.0             7750000.0               0.031977   \n",
       "1389574                 114.0             7750000.0               0.032019   \n",
       "1389579                 118.0             6900000.0               0.031981   \n",
       "1389580                 115.0             6900000.0               0.032028   \n",
       "1389581                 118.0             7250000.0               0.031990   \n",
       "1389582                 115.0             7250000.0               0.032039   \n",
       "1389589                 118.0            55600000.0               0.031918   \n",
       "1389590                 115.0            55600000.0               0.031775   \n",
       "1389593                 119.0            24500000.0               0.031992   \n",
       "1389594                 116.0            24500000.0               0.031965   \n",
       "1389599                 119.0            19900000.0               0.032013   \n",
       "1389600                 116.0            19900000.0               0.032010   \n",
       "1389603                 118.0            14400000.0               0.032044   \n",
       "1389604                 115.0            14400000.0               0.032076   \n",
       "1389607                 119.0            11350000.0               0.032045   \n",
       "1389608                 116.0            11350000.0               0.032083   \n",
       "1389615                 118.0             5900000.0               0.032050   \n",
       "1389616                 115.0             5900000.0               0.032102   \n",
       "1389617                 117.0             3100000.0               0.032055   \n",
       "1389618                 114.0             3100000.0               0.032116   \n",
       "1389619                 119.0            74500000.0               0.031962   \n",
       "1389620                 116.0            74500000.0               0.031801   \n",
       "1389633                 118.0            19975000.0               0.032024   \n",
       "1389634                 115.0            19975000.0               0.032015   \n",
       "1389637                 119.0            17300000.0               0.032036   \n",
       "1389638                 116.0            17300000.0               0.032042   \n",
       "\n",
       "         percentage_occupied_rentspace  outstanding_scheduled_balance  \\\n",
       "129                           9.044013                    16418132.66   \n",
       "130                          93.000000                    16373928.23   \n",
       "131                          93.000000                    16324415.51   \n",
       "132                          13.294962                    16282281.43   \n",
       "133                          93.000000                    16239077.27   \n",
       "134                          93.000000                    16190538.52   \n",
       "135                          11.109354                    16140901.07   \n",
       "136                           7.440000                    16094275.51   \n",
       "137                          11.071146                    16046469.95   \n",
       "138                          11.049621                    15993268.83   \n",
       "139                          11.025901                    15934643.77   \n",
       "140                          17.546049                    15882970.12   \n",
       "141                          17.707837                    15829992.92   \n",
       "142                         -42.266324                    15771551.63   \n",
       "143                         -42.292191                    15707619.03   \n",
       "144                          10.910890                    15650389.00   \n",
       "145                          10.887152                    15591719.42   \n",
       "146                          10.861173                    15527510.34   \n",
       "147                          10.832941                    15457735.81   \n",
       "148                          10.807311                    15394389.96   \n",
       "149                          10.781038                    15329453.68   \n",
       "150                          10.773902                    15258897.29   \n",
       "151                          90.000000                    15186708.04   \n",
       "152                           7.595129                    15116728.85   \n",
       "153                           7.598557                    15044998.08   \n",
       "154                           7.585581                    14967554.73   \n",
       "155                           7.566489                    14884375.81   \n",
       "156                           7.544077                    14806997.11   \n",
       "157                           7.508074                    14727685.73   \n",
       "158                           7.507638                    14642561.33   \n",
       "...                                ...                            ...   \n",
       "1389539                     217.535924                     1493445.23   \n",
       "1389540                     218.559427                     1486984.25   \n",
       "1389543                     217.768138                     1292505.92   \n",
       "1389544                     218.650976                     1286995.83   \n",
       "1389573                     219.104490                     5728000.00   \n",
       "1389574                     219.645079                     5728000.00   \n",
       "1389579                     218.870667                     4893486.48   \n",
       "1389580                     219.469330                     4877453.51   \n",
       "1389581                     218.612382                     4050000.00   \n",
       "1389582                     100.000000                     4050000.00   \n",
       "1389589                     228.910845                    40000000.00   \n",
       "1389590                     227.053552                    40000000.00   \n",
       "1389593                     223.022555                    18400000.00   \n",
       "1389594                     222.668621                    18400000.00   \n",
       "1389599                     221.821206                    14100000.00   \n",
       "1389600                     221.781998                    14100000.00   \n",
       "1389603                     220.149584                     8170140.98   \n",
       "1389604                     220.546452                     8140994.87   \n",
       "1389607                     219.795547                     6800000.00   \n",
       "1389608                     220.283520                     6800000.00   \n",
       "1389615                     219.033920                     3920000.00   \n",
       "1389616                     219.709590                     3920000.00   \n",
       "1389617                     218.572324                     2204979.55   \n",
       "1389618                     219.362561                     2197745.64   \n",
       "1389619                     230.187029                    45000000.00   \n",
       "1389620                     228.096599                    45000000.00   \n",
       "1389633                     222.207821                    14982000.00   \n",
       "1389634                     222.096102                    14982000.00   \n",
       "1389637                     221.506413                    12470953.99   \n",
       "1389638                     221.569980                    12430505.73   \n",
       "\n",
       "         origination_loan_to_value       ...        \\\n",
       "129                          67.13       ...         \n",
       "130                          67.13       ...         \n",
       "131                          67.13       ...         \n",
       "132                          67.13       ...         \n",
       "133                          67.13       ...         \n",
       "134                          67.13       ...         \n",
       "135                          67.13       ...         \n",
       "136                          67.13       ...         \n",
       "137                          67.13       ...         \n",
       "138                          67.13       ...         \n",
       "139                          67.13       ...         \n",
       "140                          67.13       ...         \n",
       "141                          67.13       ...         \n",
       "142                          67.13       ...         \n",
       "143                          67.13       ...         \n",
       "144                          67.13       ...         \n",
       "145                          67.13       ...         \n",
       "146                          67.13       ...         \n",
       "147                          67.13       ...         \n",
       "148                          67.13       ...         \n",
       "149                          67.13       ...         \n",
       "150                          67.13       ...         \n",
       "151                          67.13       ...         \n",
       "152                          67.13       ...         \n",
       "153                          67.13       ...         \n",
       "154                          67.13       ...         \n",
       "155                          67.13       ...         \n",
       "156                          67.13       ...         \n",
       "157                          67.13       ...         \n",
       "158                          67.13       ...         \n",
       "...                            ...       ...         \n",
       "1389539                       7.10       ...         \n",
       "1389540                       7.10       ...         \n",
       "1389543                      26.20       ...         \n",
       "1389544                      26.20       ...         \n",
       "1389573                      73.91       ...         \n",
       "1389574                      73.91       ...         \n",
       "1389579                      71.00       ...         \n",
       "1389580                      71.00       ...         \n",
       "1389581                      55.86       ...         \n",
       "1389582                      55.86       ...         \n",
       "1389589                      71.94       ...         \n",
       "1389590                      71.94       ...         \n",
       "1389593                      75.10       ...         \n",
       "1389594                      75.10       ...         \n",
       "1389599                      70.85       ...         \n",
       "1389600                      70.85       ...         \n",
       "1389603                      56.82       ...         \n",
       "1389604                      56.82       ...         \n",
       "1389607                      59.91       ...         \n",
       "1389608                      59.91       ...         \n",
       "1389615                      66.44       ...         \n",
       "1389616                      66.44       ...         \n",
       "1389617                      71.22       ...         \n",
       "1389618                      71.22       ...         \n",
       "1389619                      60.40       ...         \n",
       "1389620                      60.40       ...         \n",
       "1389633                      75.00       ...         \n",
       "1389634                      75.00       ...         \n",
       "1389637                      72.20       ...         \n",
       "1389638                      72.20       ...         \n",
       "\n",
       "         division_NorthEast-NewEngland  division_Other  \\\n",
       "129                                0.0             0.0   \n",
       "130                                0.0             0.0   \n",
       "131                                0.0             0.0   \n",
       "132                                0.0             0.0   \n",
       "133                                0.0             0.0   \n",
       "134                                0.0             0.0   \n",
       "135                                0.0             0.0   \n",
       "136                                0.0             0.0   \n",
       "137                                0.0             0.0   \n",
       "138                                0.0             0.0   \n",
       "139                                0.0             0.0   \n",
       "140                                0.0             0.0   \n",
       "141                                0.0             0.0   \n",
       "142                                0.0             0.0   \n",
       "143                                0.0             0.0   \n",
       "144                                0.0             0.0   \n",
       "145                                0.0             0.0   \n",
       "146                                0.0             0.0   \n",
       "147                                0.0             0.0   \n",
       "148                                0.0             0.0   \n",
       "149                                0.0             0.0   \n",
       "150                                0.0             0.0   \n",
       "151                                0.0             0.0   \n",
       "152                                0.0             0.0   \n",
       "153                                0.0             0.0   \n",
       "154                                0.0             0.0   \n",
       "155                                0.0             0.0   \n",
       "156                                0.0             0.0   \n",
       "157                                0.0             0.0   \n",
       "158                                0.0             0.0   \n",
       "...                                ...             ...   \n",
       "1389539                            0.0             0.0   \n",
       "1389540                            0.0             0.0   \n",
       "1389543                            0.0             0.0   \n",
       "1389544                            0.0             0.0   \n",
       "1389573                            0.0             0.0   \n",
       "1389574                            0.0             0.0   \n",
       "1389579                            0.0             0.0   \n",
       "1389580                            0.0             0.0   \n",
       "1389581                            0.0             0.0   \n",
       "1389582                            0.0             0.0   \n",
       "1389589                            1.0             0.0   \n",
       "1389590                            1.0             0.0   \n",
       "1389593                            0.0             0.0   \n",
       "1389594                            0.0             0.0   \n",
       "1389599                            0.0             0.0   \n",
       "1389600                            0.0             0.0   \n",
       "1389603                            0.0             0.0   \n",
       "1389604                            0.0             0.0   \n",
       "1389607                            0.0             0.0   \n",
       "1389608                            0.0             0.0   \n",
       "1389615                            1.0             0.0   \n",
       "1389616                            1.0             0.0   \n",
       "1389617                            0.0             0.0   \n",
       "1389618                            0.0             0.0   \n",
       "1389619                            0.0             0.0   \n",
       "1389620                            0.0             0.0   \n",
       "1389633                            0.0             1.0   \n",
       "1389634                            0.0             1.0   \n",
       "1389637                            0.0             0.0   \n",
       "1389638                            0.0             0.0   \n",
       "\n",
       "         division_South-Atlantic  division_South-EastSouthCentral  \\\n",
       "129                          0.0                              0.0   \n",
       "130                          0.0                              0.0   \n",
       "131                          0.0                              0.0   \n",
       "132                          0.0                              0.0   \n",
       "133                          0.0                              0.0   \n",
       "134                          0.0                              0.0   \n",
       "135                          0.0                              0.0   \n",
       "136                          0.0                              0.0   \n",
       "137                          0.0                              0.0   \n",
       "138                          0.0                              0.0   \n",
       "139                          0.0                              0.0   \n",
       "140                          0.0                              0.0   \n",
       "141                          0.0                              0.0   \n",
       "142                          0.0                              0.0   \n",
       "143                          0.0                              0.0   \n",
       "144                          0.0                              0.0   \n",
       "145                          0.0                              0.0   \n",
       "146                          0.0                              0.0   \n",
       "147                          0.0                              0.0   \n",
       "148                          0.0                              0.0   \n",
       "149                          0.0                              0.0   \n",
       "150                          0.0                              0.0   \n",
       "151                          0.0                              0.0   \n",
       "152                          0.0                              0.0   \n",
       "153                          0.0                              0.0   \n",
       "154                          0.0                              0.0   \n",
       "155                          0.0                              0.0   \n",
       "156                          0.0                              0.0   \n",
       "157                          0.0                              0.0   \n",
       "158                          0.0                              0.0   \n",
       "...                          ...                              ...   \n",
       "1389539                      0.0                              0.0   \n",
       "1389540                      0.0                              0.0   \n",
       "1389543                      0.0                              0.0   \n",
       "1389544                      0.0                              0.0   \n",
       "1389573                      0.0                              0.0   \n",
       "1389574                      0.0                              0.0   \n",
       "1389579                      1.0                              0.0   \n",
       "1389580                      1.0                              0.0   \n",
       "1389581                      0.0                              0.0   \n",
       "1389582                      0.0                              0.0   \n",
       "1389589                      0.0                              0.0   \n",
       "1389590                      0.0                              0.0   \n",
       "1389593                      0.0                              0.0   \n",
       "1389594                      0.0                              0.0   \n",
       "1389599                      0.0                              0.0   \n",
       "1389600                      0.0                              0.0   \n",
       "1389603                      0.0                              0.0   \n",
       "1389604                      0.0                              0.0   \n",
       "1389607                      0.0                              0.0   \n",
       "1389608                      0.0                              0.0   \n",
       "1389615                      0.0                              0.0   \n",
       "1389616                      0.0                              0.0   \n",
       "1389617                      1.0                              0.0   \n",
       "1389618                      1.0                              0.0   \n",
       "1389619                      0.0                              0.0   \n",
       "1389620                      0.0                              0.0   \n",
       "1389633                      0.0                              0.0   \n",
       "1389634                      0.0                              0.0   \n",
       "1389637                      0.0                              0.0   \n",
       "1389638                      0.0                              0.0   \n",
       "\n",
       "         division_South-WestSouthCentral  division_West-Mountain  \\\n",
       "129                                  1.0                     0.0   \n",
       "130                                  1.0                     0.0   \n",
       "131                                  1.0                     0.0   \n",
       "132                                  1.0                     0.0   \n",
       "133                                  1.0                     0.0   \n",
       "134                                  1.0                     0.0   \n",
       "135                                  1.0                     0.0   \n",
       "136                                  1.0                     0.0   \n",
       "137                                  1.0                     0.0   \n",
       "138                                  1.0                     0.0   \n",
       "139                                  1.0                     0.0   \n",
       "140                                  1.0                     0.0   \n",
       "141                                  1.0                     0.0   \n",
       "142                                  1.0                     0.0   \n",
       "143                                  1.0                     0.0   \n",
       "144                                  1.0                     0.0   \n",
       "145                                  1.0                     0.0   \n",
       "146                                  1.0                     0.0   \n",
       "147                                  1.0                     0.0   \n",
       "148                                  1.0                     0.0   \n",
       "149                                  1.0                     0.0   \n",
       "150                                  1.0                     0.0   \n",
       "151                                  1.0                     0.0   \n",
       "152                                  1.0                     0.0   \n",
       "153                                  1.0                     0.0   \n",
       "154                                  1.0                     0.0   \n",
       "155                                  1.0                     0.0   \n",
       "156                                  1.0                     0.0   \n",
       "157                                  1.0                     0.0   \n",
       "158                                  1.0                     0.0   \n",
       "...                                  ...                     ...   \n",
       "1389539                              0.0                     0.0   \n",
       "1389540                              0.0                     0.0   \n",
       "1389543                              0.0                     0.0   \n",
       "1389544                              0.0                     0.0   \n",
       "1389573                              1.0                     0.0   \n",
       "1389574                              1.0                     0.0   \n",
       "1389579                              0.0                     0.0   \n",
       "1389580                              0.0                     0.0   \n",
       "1389581                              0.0                     0.0   \n",
       "1389582                              0.0                     0.0   \n",
       "1389589                              0.0                     0.0   \n",
       "1389590                              0.0                     0.0   \n",
       "1389593                              1.0                     0.0   \n",
       "1389594                              1.0                     0.0   \n",
       "1389599                              0.0                     0.0   \n",
       "1389600                              0.0                     0.0   \n",
       "1389603                              0.0                     0.0   \n",
       "1389604                              0.0                     0.0   \n",
       "1389607                              0.0                     0.0   \n",
       "1389608                              0.0                     0.0   \n",
       "1389615                              0.0                     0.0   \n",
       "1389616                              0.0                     0.0   \n",
       "1389617                              0.0                     0.0   \n",
       "1389618                              0.0                     0.0   \n",
       "1389619                              0.0                     0.0   \n",
       "1389620                              0.0                     0.0   \n",
       "1389633                              0.0                     0.0   \n",
       "1389634                              0.0                     0.0   \n",
       "1389637                              1.0                     0.0   \n",
       "1389638                              1.0                     0.0   \n",
       "\n",
       "         division_West-Pacific  interestonly_N  interestonly_P  interestonly_Y  \n",
       "129                        0.0             1.0             0.0             0.0  \n",
       "130                        0.0             1.0             0.0             0.0  \n",
       "131                        0.0             1.0             0.0             0.0  \n",
       "132                        0.0             1.0             0.0             0.0  \n",
       "133                        0.0             1.0             0.0             0.0  \n",
       "134                        0.0             1.0             0.0             0.0  \n",
       "135                        0.0             1.0             0.0             0.0  \n",
       "136                        0.0             1.0             0.0             0.0  \n",
       "137                        0.0             1.0             0.0             0.0  \n",
       "138                        0.0             1.0             0.0             0.0  \n",
       "139                        0.0             1.0             0.0             0.0  \n",
       "140                        0.0             1.0             0.0             0.0  \n",
       "141                        0.0             1.0             0.0             0.0  \n",
       "142                        0.0             1.0             0.0             0.0  \n",
       "143                        0.0             1.0             0.0             0.0  \n",
       "144                        0.0             1.0             0.0             0.0  \n",
       "145                        0.0             1.0             0.0             0.0  \n",
       "146                        0.0             1.0             0.0             0.0  \n",
       "147                        0.0             1.0             0.0             0.0  \n",
       "148                        0.0             1.0             0.0             0.0  \n",
       "149                        0.0             1.0             0.0             0.0  \n",
       "150                        0.0             1.0             0.0             0.0  \n",
       "151                        0.0             1.0             0.0             0.0  \n",
       "152                        0.0             1.0             0.0             0.0  \n",
       "153                        0.0             1.0             0.0             0.0  \n",
       "154                        0.0             1.0             0.0             0.0  \n",
       "155                        0.0             1.0             0.0             0.0  \n",
       "156                        0.0             1.0             0.0             0.0  \n",
       "157                        0.0             1.0             0.0             0.0  \n",
       "158                        0.0             1.0             0.0             0.0  \n",
       "...                        ...             ...             ...             ...  \n",
       "1389539                    0.0             1.0             0.0             0.0  \n",
       "1389540                    0.0             1.0             0.0             0.0  \n",
       "1389543                    0.0             1.0             0.0             0.0  \n",
       "1389544                    0.0             1.0             0.0             0.0  \n",
       "1389573                    0.0             0.0             1.0             0.0  \n",
       "1389574                    0.0             0.0             1.0             0.0  \n",
       "1389579                    0.0             1.0             0.0             0.0  \n",
       "1389580                    0.0             1.0             0.0             0.0  \n",
       "1389581                    1.0             0.0             0.0             1.0  \n",
       "1389582                    1.0             0.0             0.0             1.0  \n",
       "1389589                    0.0             0.0             1.0             0.0  \n",
       "1389590                    0.0             0.0             1.0             0.0  \n",
       "1389593                    0.0             0.0             1.0             0.0  \n",
       "1389594                    0.0             0.0             1.0             0.0  \n",
       "1389599                    0.0             0.0             1.0             0.0  \n",
       "1389600                    0.0             0.0             1.0             0.0  \n",
       "1389603                    1.0             1.0             0.0             0.0  \n",
       "1389604                    1.0             1.0             0.0             0.0  \n",
       "1389607                    1.0             0.0             1.0             0.0  \n",
       "1389608                    1.0             0.0             1.0             0.0  \n",
       "1389615                    0.0             0.0             1.0             0.0  \n",
       "1389616                    0.0             0.0             1.0             0.0  \n",
       "1389617                    0.0             1.0             0.0             0.0  \n",
       "1389618                    0.0             1.0             0.0             0.0  \n",
       "1389619                    1.0             0.0             0.0             1.0  \n",
       "1389620                    1.0             0.0             0.0             1.0  \n",
       "1389633                    0.0             0.0             1.0             0.0  \n",
       "1389634                    0.0             0.0             1.0             0.0  \n",
       "1389637                    0.0             1.0             0.0             0.0  \n",
       "1389638                    0.0             1.0             0.0             0.0  \n",
       "\n",
       "[279310 rows x 52 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test.colu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00035086463069707491"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Logistic.predict(Test.drop(\"Target_Response\",axis=1)).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lm.LogisticRegression().fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Target_Response    0.021976\n",
       "dtype: float64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test_Target = Test[[\"Target_Response\"]]\n",
    "Test_Target.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Target_Response    0.021976\n",
       "dtype: float64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test[[\"Target_Response\"]].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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